Library Automation and Digital Archive
LONTAR
Fakultas Ilmu Komputer
Universitas Indonesia

Pencarian Sederhana

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Call Number SEM-364
Collection Type Indeks Artikel prosiding/Sem
Title A collaborative names recommendation in the twitter environment based on location. ( hal. 119-124 )
Author Normaslina Jamil, Arifah Che Alhadi, Shahrul Azman Noah;
Publisher 2011 International conference on semantic technology and information retrieval 28-29 June 2011 Putrajaya Malaysia
Subject Collaborative filtering, recommender system, twitter.
Location
Lokasi : Perpustakaan Fakultas Ilmu Komputer
Nomor Panggil ID Koleksi Status
SEM-364 TERSEDIA
Tidak ada review pada koleksi ini: 47650
Friendster,facebook,twitter and many other microblogs have been introduced since 2004.These web 2.0 application have become a powerful tool for communication.Each social web site has milions of users whose interact with each other regardless of their location and distance.Therefore,the mechanism of recommendation system for these sites is important for users to find suitable friends.Name recommendation should be made based on the conceps of homophily which stated that relationships between individuals who have in common is higher than individuals who have nothing in common.Twitter is one of the popular social web sites that were developed in 2006.Many of the twitter users are passive users.They just follow other users but on the other side they do not have many followers.This problem arises because reciprocal relationship is not required in twitter.To overcome this problem,a recommendation system can help users in searching friends by taking info account reciprocal relationship.The main goal of this study is to use collaborative filtering techniques to recommed names based on geographical location.User's location is taken from the user's profile by using coordinates of latitude and longitude.Celebrities profile data sets provided by the korea advanced institute of scince and technology (KAIST) are taken for testing purpose.The result of the testing indicates the potential of exploiting geographical locations in collaboratively recommending names within twitter environment. Keywords: Collaborative filtering, recommender system, twitter.